Green extraction of bioactive components from carrot industry waste and evaluation of spent residue as an energy source
Bibliographic record
Abstract
Carrot processing industries produce 25-30% of waste in the form of carrot rejects, peels, and pomace which contain a large amount of high-value bioactive components. Green extraction of the bioactive components from carrot rejects with green solvents using closed-vessel energy-intensive microwave-assisted extraction was the objective of this work. In this work, three experimental studies were implemented. One uses 8 different green solvents for maximum yield of bioactive using green technology, and the other for the optimization of Microwave-assisted Extraction (MAE) parameters to enhance the bioactive components yield. Response Surface Methodology was employed to optimize the processing parameters including temperature, time, solid to solvent ratio, and solvent type. The optimized extraction conditions: treatment temperature of 50 °C for 5 min gave a significantly higher yield of total carotenoids (192.81 ± 0.32 mg carotenoids/100 g DW), total phenolic (78.12 ± 0.35 g GAE/100 g DW), and antioxidants by FRAP (5889.63 ± 0.47 mM TE/100 g DW), ABTS (1143.65 ± 0.81 mM TE/100 g DW), and DPPH (823.14 ± 0.54 mM TE/100 g DW) using a solvent combination of hexane and ethanol (1:3) with solid to solvent ratio of 1:40 (w/v). This green technology in combination with GRAS solvents promoted the best recovery of bioactive from carrot rejects. Moreover, the solid residue remained after the extraction of bioactive components exhibited higher carbon content (46.5%) and calorific value (16.32 MJ/kg), showcasing its potential to be used as an energy source.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".